Abstract
Abstract. Atmospheric measurements from the Arctic Summer Cloud Ocean Study (ASCOS) are used to evaluate the performance of three atmospheric reanalyses (European Centre for Medium Range Weather Forecasting (ECMWF)-Interim reanalysis, National Center for Environmental Prediction (NCEP)-National Center for Atmospheric Research (NCAR) reanalysis, and NCEP-DOE (Department of Energy) reanalysis) and two global climate models (CAM5 (Community Atmosphere Model 5) and NASA GISS (Goddard Institute for Space Studies) ModelE2) in simulation of the high Arctic environment. Quantities analyzed include near surface meteorological variables such as temperature, pressure, humidity and winds, surface-based estimates of cloud and precipitation properties, the surface energy budget, and lower atmospheric temperature structure. In general, the models perform well in simulating large-scale dynamical quantities such as pressure and winds. Near-surface temperature and lower atmospheric stability, along with surface energy budget terms, are not as well represented due largely to errors in simulation of cloud occurrence, phase and altitude. Additionally, a development version of CAM5, which features improved handling of cloud macro physics, has demonstrated to improve simulation of cloud properties and liquid water amount. The ASCOS period additionally provides an excellent example of the benefits gained by evaluating individual budget terms, rather than simply evaluating the net end product, with large compensating errors between individual surface energy budget terms that result in the best net energy budget.
Highlights
Both modeling and observational studies demonstrate that the Arctic is warming at a rate faster than the rest of the globe (e.g., IPCC, 2007; Serreze et al, 2009; Serreze and Francis, 2006; Rigor et al, 2000)
We evaluate some of the tools described above using measurements obtained during the Arctic Summer Cloud Ocean Study (ASCOS, Tjernström et al, 2013)
The model results have been linearly interpolated in space to the exact location of the Oden at a given time, reducing the influence of resolution that would result from a nearest grid cell comparison, and eliminating the influence of jumping between grid boxes with the movement of Oden during the campaign
Summary
Both modeling and observational studies demonstrate that the Arctic is warming at a rate faster than the rest of the globe (e.g., IPCC, 2007; Serreze et al, 2009; Serreze and Francis, 2006; Rigor et al, 2000). Zib et al (2012) used measurements from the Baseline Surface Radiation Network (BSRN) stations at Barrow and Ny-Alesund (78.9◦ N, 11.9◦ E) to evaluate cloud and radiative properties in the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA, Rienecker et al, 2011), the NCEP Climate Forecast System Reanalysis (CFSR, Saha et al, 2010), the National Oceanographic and Atmospheric Administration (NOAA) Twentieth Century Reanalysis Project (20CR, Compo et al, 2011), the ECMWF-Interim reanalysis (herafter ERA-I, Dee et al, 2011), and the NCEP-Department of Energy (DOE) reanalysis (hereafter R-2, Kanamitsu et al, 2002) This generation of reanalyses demonstrates large differences in cloud occurrence from one product to the including inconsistencies between relative cloud amounts from one site to the other. ASCOS measurements represent an independent data set that can be used to directly evaluate reanalysis model performance
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